Development and optimisation of micromechanical testing techniques to study the properties of meniscal tissue

Maritz, Jared; Murphy, Francesca; Dragnevski, Kalin I.; Barrera, Olga

doi:10.1016/j.matpr.2020.05.807

Cited by 7 publications

(7 citation statements)

References 11 publications

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“… 6 Following this, dog bone samples were stamped and tested in uniaxial tension using a previously developed method. 16 Dog bone samples were taken at various vertical and radial depths. In total, 8 samples were tested from the internal region, 5 from the femoral layer and 2 from the tibial layer.…”

Section: Methodsmentioning

confidence: 99%

“…Comparisons between anterior and posterior horns and the central body are less evident, although the horns have been reported to be stiffer. This work presents extensive mechanical testing results performed following a robust and repeatable procedure for both extracting samples 6 and executing the experiments 16 for all of the porcine menisci analysed.…”

Section: Introductionmentioning

confidence: 99%

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The Functionally Grading Elastic and Viscoelastic Properties of the Body Region of the Knee Meniscus

et al. 2021

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The knee meniscus is a highly porous structure which exhibits a grading architecture through the depth of the tissue. The superficial layers on both femoral and tibial sides are constituted by a fine mesh of randomly distributed collagen fibers while the internal layer is constituted by a network of collagen channels of a mean size of 22.14 $$\mu $$ μ m aligned at a $$30^{\circ }$$ 30 ∘ inclination with respect to the vertical. Horizontal dog-bone samples extracted from different depths of the tissue were mechanically tested in uniaxial tension to examine the variation of elastic and viscoelastic properties across the meniscus. The tests show that a random alignment of the collagen fibers in the superficial layers leads to stiffer mechanical responses (E = 105 and 189 MPa) in comparison to the internal regions (E = 34 MPa). All regions exhibit two modes of relaxation at a constant strain ($$\tau _1 = 6.4$$ τ 1 = 6.4 to 7.7 s, $$\tau _2$$ τ 2 = 49.9 to 59.7 s).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Functionally Grading Elastic and Viscoelastic Properties of the Body Region of the Knee Meniscus

et al. 2021

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“…Cartilage and meniscus were modelled as neo-Hookean solids. Cartilage was assumed to be incompressible (C10 = 1.667, D1 = 0) while the meniscus was divided into three layers [25], following the experimental results collected in [22, 26, 27, 28]. The two outer layers were approximately 0.2 mm thick, stiffer and nearly incompressible (C10 = 2.614, D1 = 0.008).…”

Section: Methodsmentioning

confidence: 99%

Image-based parametric finite element modelling for studying contact mechanics in human knee joints

Readioff,

Seil,

Mouton

et al. 2023

Preprint

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PurposeThis study presents a framework for generating patient-specific finite element models, parameterised and optimised for contact mechanics from computed tomography (CT) scans, by avoiding the segmentation step usually employed to transform medical images into 3D models. Two morphological parameters affecting contact mechanics were investigated in the framework development: tibial cartilage thickness and tibial spine height. This study explores the effect of the interplay of these parameters in load sharing between meniscus and articulating cartilage, meniscal posterior and anterior roots strain and menisci kine-matics.MethodsMorphological measurements from four knee CT scans were collected, such as the maximum thickness of the tibial cartilage (ranging from 1.1 to 5.2 mm), the height of the tibial spine (ranging from 3.55 to 10.1 mm), and the width of the tibial plateau in both the coronal (ranging from 27.3 to 36.17 mm) and sagittal (ranging from 31.79 to 53.77 mm) planes. These measurements were taken for the lateral tibial plateau for both left and right knees. Subsequently, three finite element (FE) models were generated, comprising lateral tibial plateaus, lateral femoral condyle and lateral meniscus. The tibial cartilage thickness was kept at a constant value of 1 mm while varying the tibial spine height within the range measured from the CT images. This resulted in three FE models with varying spine heights, categorised as large (height = 7.42 mm), medium (height = 4.25 mm), and small (height = 1.63 mm) tibial spine heights. The menisci in the FE models were generated to be congruent with the tibial plateau. For the first time, this study advances the representations of the knee menisci microstructure in FE modelling, such that we have generated meniscus FE models with three layers of a hyperelastic model in which layer thickness and layer-specific hyperelastic material parameters are derived from our previous experimental work.ResultsThe load sharing between the meniscus and articular cartilage was not sensitive to the varying tibial spine heights. In all three FE models, cartilage carried more than 90% of the applied load. However, the meniscus kinematics and root strains varied considerably with changing tibial spine heights. The small tibial spine height model predicted the highest meniscus movements (8.12 and 9.33 mm in the radial and circumferential directions, respectively) and the highest root strain (21.92 and 22.19 mm/mm in the anterior and posterior roots, respectively).ConclusionOur framework can generate finite element models of patients’ knees using clinical data (i.e., CT scans) without the need for lengthy image segmentation. This process is not only time-efficient but also independent of imaging operators. The models converge quickly (¿30 minutes on 2 cores) using an implicit solver with non-linear geometry and have the capability to predict contact mechanics between the articulating surfaces, meniscus kinematics and root strains. The modelling strategy presented here can provide valuable insights into predicting changes in the mechanics of soft tissues in the knee joint. It is particularly useful for investigating injury and surgical mechanisms related to the meniscus.

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“…Some of the key unknowns or variables that can affect its behaviour include: the exact mechanical properties and their space-dependency; the properties of the hydraulic fluid used in the damper, such as viscosity, density, and compressibility; internal architecture at different scales; loading condition and coupled solid deformation and fluid flow, the type and magnitude of the load applied to the damper will determine how it responds. While studies based on advanced imaging techniques, mechanical testing and models have been devoted to the nano-micro internal structure (collagen channels) and the functionally graded mechanical material properties [7][8][9][10][11][12][13] , no study so far covers: (a) the fluid flow behaviour (Darcian, non Darcian) and (b) fluid properties, such as Reynold's number inside channels due to a range of inlet conditions in the meniscus. (c) Also, no study presents permeability calculations based on micro-computed fluid dynamics simulations.…”

Section: Introductionmentioning

confidence: 99%

On the Characteristics of Natural Hydraulic Dampers: An Image-Based Approach to Study the Fluid Flow Behaviour Inside the Human Meniscal Tissue

Waghorne,

Bonomo,

Rabbani

et al. 2023

Preprint

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Development and optimisation of micromechanical testing techniques to study the properties of meniscal tissue

Cited by 7 publications

References 11 publications

The Functionally Grading Elastic and Viscoelastic Properties of the Body Region of the Knee Meniscus

The Functionally Grading Elastic and Viscoelastic Properties of the Body Region of the Knee Meniscus

Image-based parametric finite element modelling for studying contact mechanics in human knee joints

On the Characteristics of Natural Hydraulic Dampers: An Image-Based Approach to Study the Fluid Flow Behaviour Inside the Human Meniscal Tissue

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